Summary of Characterizing and Evaluating the Reliability Of Llms Against Jailbreak Attacks, by Kexin Chen et al.
Characterizing and Evaluating the Reliability of LLMs against Jailbreak Attacks
by Kexin Chen, Yi Liu, Dongxia Wang, Jiaying Chen, Wenhai Wang
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have revolutionized content generation, but their potential for harm highlights the need for safeguards and ethics considerations. Despite measures to ensure social responsibility, “jailbreaking” prompts can elicit harmful responses from models, posing a significant threat to trustworthy use. To address this challenge, we introduce a comprehensive evaluation framework and conduct a large-scale empirical experiment. We assess 10 cutting-edge jailbreak strategies across three categories, using metrics like Attack Success Rate (ASR), Toxicity Score, Fluency, Token Length, and Grammatical Errors. Our reliability score reveals that all tested LLMs are vulnerable to certain attack strategies, underscoring the need for security enhancements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can create content. But they can also be used to say mean or harmful things if someone wants them to. This is a big problem because it could hurt people’s feelings or even cause harm. To fix this, researchers came up with a plan to test these models and see how well they can handle bad prompts. They looked at 10 different ways that someone might try to make the model say something mean, and used special tools to measure how well each model did. The results showed that all of the models were vulnerable to some kind of attack. This means we need to be careful when using these models and make sure they’re not saying anything bad. |
Keywords
» Artificial intelligence » Token
